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Probabilistic detection of GNSS spoofing using opportunistic information

Wenjie Liu, Panos Papadimitratos

TL;DR

The paper tackles GNSS spoofing by exploiting opportunistic information from networks and on-board sensors to detect attacks without cryptographic protections. It introduces PDS, which combines a motion-constrained local polynomial regression with a Gaussian-process model of uncertainty, and fuses them into a Neyman-Pearson-style likelihood detector. The approach supports networks-only, sensors-only, or all-sources configurations and demonstrates improved true-positive rates, reduced detection delays, and better alternative-position accuracy across multiple datasets compared to baseline methods. This probabilistic framework enhances robustness to various spoofing strategies and offers practical applicability for mobile platforms relying on GNSS in adversarial environments. The work also discusses efficiency considerations and directions for future work, including extending the attacker model to network-layer threats.

Abstract

Global Navigation Satellite Systems (GNSS) are integrated into many devices. However, civilian GNSS signals are usually not cryptographically protected. This makes attacks that forge signals relatively easy. Considering modern devices often have network connections and onboard sensors, the proposed here Probabilistic Detection of GNSS Spoofing (PDS) scheme is based on such opportunistic information. PDS has at its core two parts. First, a regression problem with motion model constraints, which equalizes the noise of all locations considering the motion model of the device. Second, a Gaussian process, that analyzes statistical properties of location data to construct uncertainty. Then, a likelihood function, that fuses the two parts, as a basis for a Neyman-Pearson lemma (NPL)-based detection strategy. Our experimental evaluation shows a performance gain over the state-of-the-art, in terms of attack detection effectiveness.

Probabilistic detection of GNSS spoofing using opportunistic information

TL;DR

The paper tackles GNSS spoofing by exploiting opportunistic information from networks and on-board sensors to detect attacks without cryptographic protections. It introduces PDS, which combines a motion-constrained local polynomial regression with a Gaussian-process model of uncertainty, and fuses them into a Neyman-Pearson-style likelihood detector. The approach supports networks-only, sensors-only, or all-sources configurations and demonstrates improved true-positive rates, reduced detection delays, and better alternative-position accuracy across multiple datasets compared to baseline methods. This probabilistic framework enhances robustness to various spoofing strategies and offers practical applicability for mobile platforms relying on GNSS in adversarial environments. The work also discusses efficiency considerations and directions for future work, including extending the attacker model to network-layer threats.

Abstract

Global Navigation Satellite Systems (GNSS) are integrated into many devices. However, civilian GNSS signals are usually not cryptographically protected. This makes attacks that forge signals relatively easy. Considering modern devices often have network connections and onboard sensors, the proposed here Probabilistic Detection of GNSS Spoofing (PDS) scheme is based on such opportunistic information. PDS has at its core two parts. First, a regression problem with motion model constraints, which equalizes the noise of all locations considering the motion model of the device. Second, a Gaussian process, that analyzes statistical properties of location data to construct uncertainty. Then, a likelihood function, that fuses the two parts, as a basis for a Neyman-Pearson lemma (NPL)-based detection strategy. Our experimental evaluation shows a performance gain over the state-of-the-art, in terms of attack detection effectiveness.
Paper Structure (36 sections, 2 theorems, 34 equations, 12 figures, 1 table, 3 algorithms)

This paper contains 36 sections, 2 theorems, 34 equations, 12 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Terrestrial Positioning
  4. GNSS Spoofing Attacks
  5. GNSS Spoofing Detection
  6. System Model and Adversary
  7. System Model
  8. Adversary
  9. Problem Statement
  10. Proposed Scheme
  11. Scheme Outline
  12. Rolling for Screening and Detection
  13. Data Collection
  14. Window Size
  15. Processing $S$
  16. ...and 21 more sections

Key Result

Theorem 1

The estimator $\hat{\mathbf{p}}_m(t)$ can estimate $\mathbf{p}_m(t)$ in polynomial time, i.e., the problem $\mathcal{P}$ is a polynomial time problem.

Figures (12)

  • Figure 1: A two-dimensional example of location information from the existing infrastructures.
  • Figure 2: System and adversary model illustration.
  • Figure 3: System overview of PDS.
  • Figure 4: Estimated trace from local polynomial regression (tiled view).
  • Figure 5: Gaussian process for modeling residual part of estimated positions (tiled view).
  • ...and 7 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof